Day-ahead photovoltaic (PV) forecasting must resolve the coexistence of a smooth diurnal generation envelope and shorter-scale fluctuations while providing uncertainty information suitable for operational decision making. This paper develops a multi-resolution Wavelet–CNN–LSTM framework for 24 h ahead PV power forecasting and evaluates it using a five-year PVGIS-derived dataset for a 334.116 MW Rabigh case study. The forecasting information set is restricted to the preceding 24 h of PV power and meteorological observations, together with deterministic target-day temporal variables. A validation study selects a Haar level-2 decomposition of PV history into $A_2$, $D_2$, and $D_1$ components, which are processed through parallel convolutional branches before recurrent fusion. The resulting network contains 87,032 trainable parameters. On the 2023 temporal evaluation period, the proposed model achieves an RMSE of 17.228 MW, $R^2=0.9641$, and a daylight RMSE of 24.636 MW, corresponding to 25.69% forecast skill relative to 24 h seasonal persistence. A 7-day moving-block bootstrap confirms clear RMSE reductions relative to persistence and XGBoost, while the intervals for LSTM and CNN–LSTM include zero, indicating statistically comparable performance among the strongest recurrent models. Probabilistic forecasts obtained from 100-pass Monte Carlo dropout yield daylight PICP = 0.9279, MPIW = 84.06 MW, and Winkler score = 122.08 for nominal 90% intervals; an independent conformal calibration set requires no additional widening of the neural interval. The results show that multi-resolution representation can improve squared-error performance without sacrificing model compactness, while calibrated uncertainty provides a complementary measure of forecast reliability.